The Impact of GenAI and Its Implications for Data Scientists

GenAI systems affect how we work. This general notion is well known. However, we are still unaware of the exact impact of GenAI. For example, how much do these tools affect our work? Do they have a larger impact on certain tasks? What does this mean for us in our daily work?

To answer these questions, Anthropic released a study based on millions of anonymized conversations on Claude.ai. The study provides data on how GenAI is incorporated into real-world tasks and reveals actual GenAI usage patterns.

In this article, I will go through the four main findings of the study. Based on the findings I will derive how GenAI changes our work and what skills we need in the future.

Main findings

GenAI is mostly used for software development and technical writing tasks, reaching almost 50 % of all tasks. This is likely due to LLMs being mostly text-based and thus being less useful for certain tasks.

GenAI has a stronger impact on some groups of occupations than others.More than one-third of occupations use GenAI in at least a quarter of their tasks. In contrast, only 4 % of occupations use it for more than three-quarters of their tasks. We can see that only very few occupations use GenAI across most of their tasks. This suggests that no job is being entirely automated.

GenAI is used for augmentation rather than automation, i.e., 57% vs 43 % of the tasks. But most occupations use both, augmentation and automation across tasks. Here, augmentation means the user collaborates with the GenAI to enhance their capabilities. Automation, in contrast, refers to tasks in which the GenAI directly performs the task. However, the authors guess that the share of augmentation is even higher as users might adjust GenAI answers outside of the chat window. Hence, what seems to be automation is actually augmentation. The results suggest that GenAI serves as an efficiency tool and a collaborative partner, resulting in improved productivity. These results align very well with my own experience. I mostly use GenAI tools to augment my work instead of automating tasks. In the article below you can see how GenAI tools have increased my productivity and what I use them for daily.

GenAI is mostly used for tasks associated with mid-to-high-wage occupations, such as data scientists. In contrast, the lowest and highest-paid roles show a much lower usage of GenAI. The authors conclude that this is due to the current limits of GenAI capabilities and practical barriers when it comes to using GenAI.

Overall, the study suggests that occupations will rather evolve than disappear. This is because of two reasons. First, GenAI integration remains selective rather than comprehensive within most occupations. Although many jobs use GenAI, the tools are only used selectively for certain tasks. Second, the study saw a clear preference for augmentation over automation. Hence, GenAI serves as an efficiency tool and a collaborative partner.

Limitations

Before we can derive the implications of GenAI, we should look at the limitations of the study:

• It is unknown how the users used the responses. Are they copy-pasting code snippets uncritically or editing them in their IDE? Hence, some conversations that look like automation might have been augmentation instead.
• The authors only used conversations from Claude.ai’s chat but not from API or Enterprise users. Hence, the dataset used in the analysis shows only a fraction of actual GenAI usage.
• Automating the classification might have led to the wrong classification of conversations. However, due to the large amount of conversation used the impact should be rather small.
• Claude being only text-based restricts the tasks and thus might exclude certain jobs.
• Claude is advertised as a state-of-the-art coding model thus attracting mostly users for coding tasks.

Overall, the authors conclude that their dataset is not a representative sample of GenAI use in general. Thus, we should handle and interpret the results with care. Despite the study’s limitations, we can see some implications from the impact of GenAI on our work, particularly as Data Scientists.

Implications

The study shows that GenAI has the potential to reshape jobs and we can already see its impact on our work. Moreover, GenAI is rapidly evolving and still in the early stages of workplace integration.

Thus, we should be open to these changes and adapt to them.

Most importantly, we must stay curious, adaptive, and willing to learn. In the field of Data Science changes happen regularly. With GenAI tools change will happen even more frequently. Hence, we must stay up-to-date and use the tools to support us in this journey.

Currently, GenAI has the potential to enhance our capabilities instead of automating them.

Hence, we should focus on developing skills that complement GenAI. We need skills to augment workflows effectively in our work and analytical tasks. These skills lie in areas with low penetration of GenAI. This includes human interaction, strategic thinking, and nuanced decision-making. This is where we can stand out.

Moreover, skills such as critical thinking, complex problem-solving, and judgment will remain highly valuable. We must be able to ask the right questions, interpret the output of LLMs, and take action based on the answers.

Moreover, GenAI will not replace our collaboration with colleagues in projects. Hence, improving our emotional intelligence will help us to work together effectively.

Conclusion

GenAI is rapidly evolving and still in the early stages of workplace integration. However, we can already see some implications from the impact of GenAI on our work.

In this article, I showed you the main findings of a recent study from Anthropic on the use of their LLMs. Based on the results, I showed you the implications for Data Scientists and what skills might become more important.

I hope that you find this article useful and that it will help you become a better Data Scientist.

See you in my next article.